{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import product\n",
    "from itertools import product as product\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mg:\\git仓库代码管理\\test\\finance.ipynb Cell 2'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/g%3A/git%E4%BB%93%E5%BA%93%E4%BB%A3%E7%A0%81%E7%AE%A1%E7%90%86/test/finance.ipynb#ch0000001?line=0'>1</a>\u001b[0m data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(\u001b[39m'\u001b[39m\u001b[39mdata.csv\u001b[39m\u001b[39m'\u001b[39m, index_col\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, parse_dates\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/git%E4%BB%93%E5%BA%93%E4%BB%A3%E7%A0%81%E7%AE%A1%E7%90%86/test/finance.ipynb#ch0000001?line=1'>2</a>\u001b[0m data\u001b[39m.\u001b[39mhead()\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=304'>305</a>\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=305'>306</a>\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=306'>307</a>\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39marguments),\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=307'>308</a>\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=308'>309</a>\u001b[0m         stacklevel\u001b[39m=\u001b[39mstacklevel,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=309'>310</a>\u001b[0m     )\n\u001b[1;32m--> <a href='file:///g%3A/python/lib/site-packages/pandas/util/_decorators.py?line=310'>311</a>\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:680\u001b[0m, in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=664'>665</a>\u001b[0m kwds_defaults \u001b[39m=\u001b[39m _refine_defaults_read(\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=665'>666</a>\u001b[0m     dialect,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=666'>667</a>\u001b[0m     delimiter,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=675'>676</a>\u001b[0m     defaults\u001b[39m=\u001b[39m{\u001b[39m\"\u001b[39m\u001b[39mdelimiter\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39m,\u001b[39m\u001b[39m\"\u001b[39m},\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=676'>677</a>\u001b[0m )\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=677'>678</a>\u001b[0m kwds\u001b[39m.\u001b[39mupdate(kwds_defaults)\n\u001b[1;32m--> <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=679'>680</a>\u001b[0m \u001b[39mreturn\u001b[39;00m _read(filepath_or_buffer, kwds)\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:575\u001b[0m, in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=571'>572</a>\u001b[0m _validate_names(kwds\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m))\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=573'>574</a>\u001b[0m \u001b[39m# Create the parser.\u001b[39;00m\n\u001b[1;32m--> <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=574'>575</a>\u001b[0m parser \u001b[39m=\u001b[39m TextFileReader(filepath_or_buffer, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwds)\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=576'>577</a>\u001b[0m \u001b[39mif\u001b[39;00m chunksize \u001b[39mor\u001b[39;00m iterator:\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=577'>578</a>\u001b[0m     \u001b[39mreturn\u001b[39;00m parser\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:933\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=929'>930</a>\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moptions[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwds[\u001b[39m\"\u001b[39m\u001b[39mhas_index_names\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=931'>932</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles: IOHandles \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m--> <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=932'>933</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_engine(f, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mengine)\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\io\\parsers\\readers.py:1217\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[1;34m(self, f, engine)\u001b[0m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1212'>1213</a>\u001b[0m     mode \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mrb\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1213'>1214</a>\u001b[0m \u001b[39m# error: No overload variant of \"get_handle\" matches argument types\u001b[39;00m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1214'>1215</a>\u001b[0m \u001b[39m# \"Union[str, PathLike[str], ReadCsvBuffer[bytes], ReadCsvBuffer[str]]\"\u001b[39;00m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1215'>1216</a>\u001b[0m \u001b[39m# , \"str\", \"bool\", \"Any\", \"Any\", \"Any\", \"Any\", \"Any\"\u001b[39;00m\n\u001b[1;32m-> <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1216'>1217</a>\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39m=\u001b[39m get_handle(  \u001b[39m# type: ignore[call-overload]\u001b[39;49;00m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1217'>1218</a>\u001b[0m     f,\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1218'>1219</a>\u001b[0m     mode,\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1219'>1220</a>\u001b[0m     encoding\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1220'>1221</a>\u001b[0m     compression\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mcompression\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1221'>1222</a>\u001b[0m     memory_map\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mmemory_map\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mFalse\u001b[39;49;00m),\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1222'>1223</a>\u001b[0m     is_text\u001b[39m=\u001b[39;49mis_text,\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1223'>1224</a>\u001b[0m     errors\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mencoding_errors\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mstrict\u001b[39;49m\u001b[39m\"\u001b[39;49m),\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1224'>1225</a>\u001b[0m     storage_options\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49moptions\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39;49m\u001b[39mstorage_options\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mNone\u001b[39;49;00m),\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1225'>1226</a>\u001b[0m )\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1226'>1227</a>\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m   <a href='file:///g%3A/python/lib/site-packages/pandas/io/parsers/readers.py?line=1227'>1228</a>\u001b[0m f \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandles\u001b[39m.\u001b[39mhandle\n",
      "File \u001b[1;32mG:\\python\\lib\\site-packages\\pandas\\io\\common.py:789\u001b[0m, in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=783'>784</a>\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(handle, \u001b[39mstr\u001b[39m):\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=784'>785</a>\u001b[0m     \u001b[39m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=785'>786</a>\u001b[0m     \u001b[39m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=786'>787</a>\u001b[0m     \u001b[39mif\u001b[39;00m ioargs\u001b[39m.\u001b[39mencoding \u001b[39mand\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mb\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m ioargs\u001b[39m.\u001b[39mmode:\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=787'>788</a>\u001b[0m         \u001b[39m# Encoding\u001b[39;00m\n\u001b[1;32m--> <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=788'>789</a>\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=789'>790</a>\u001b[0m             handle,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=790'>791</a>\u001b[0m             ioargs\u001b[39m.\u001b[39;49mmode,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=791'>792</a>\u001b[0m             encoding\u001b[39m=\u001b[39;49mioargs\u001b[39m.\u001b[39;49mencoding,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=792'>793</a>\u001b[0m             errors\u001b[39m=\u001b[39;49merrors,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=793'>794</a>\u001b[0m             newline\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=794'>795</a>\u001b[0m         )\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=795'>796</a>\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=796'>797</a>\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m    <a href='file:///g%3A/python/lib/site-packages/pandas/io/common.py?line=797'>798</a>\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(handle, ioargs\u001b[39m.\u001b[39mmode)\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data.csv'"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('data.csv', index_col=0, parse_dates=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>,\n",
       "       <AxesSubplot:xlabel='Date'>, <AxesSubplot:xlabel='Date'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x864 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(figsize=(10, 12), subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "      <th>GDX</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>23.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.79</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3.53</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-1.35</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-2.44</td>\n",
       "      <td>-1.88</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.62</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>0.00</td>\n",
       "      <td>19.85</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>3.41</td>\n",
       "      <td>0.48</td>\n",
       "      <td>4.55</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-6.60</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>-2.75</td>\n",
       "      <td>-2.02</td>\n",
       "      <td>-1.79</td>\n",
       "      <td>-52.52</td>\n",
       "      <td>-4.48</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>-37.81</td>\n",
       "      <td>3.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.49</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>-0.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>2.26</td>\n",
       "      <td>0.69</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>27.94</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.60</td>\n",
       "      <td>5.99</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-6.36</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>-0.27</td>\n",
       "      <td>-1.54</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>-30.58</td>\n",
       "      <td>-1.40</td>\n",
       "      <td>-2.25</td>\n",
       "      <td>-23.43</td>\n",
       "      <td>1.99</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-7.02</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>1.34</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.49</td>\n",
       "      <td>40.94</td>\n",
       "      <td>3.24</td>\n",
       "      <td>1.54</td>\n",
       "      <td>16.68</td>\n",
       "      <td>-1.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.74</td>\n",
       "      <td>0.12</td>\n",
       "      <td>-0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>-0.39</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-1.65</td>\n",
       "      <td>-2.85</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.06</td>\n",
       "      <td>-0.76</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.37</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AAPL.O  MSFT.O  INTC.O  AMZN.O  GS.N   SPY   .SPX  .VIX  EUR=  \\\n",
       "Date                                                                        \n",
       "2010-01-01     NaN     NaN     NaN     NaN   NaN   NaN    NaN   NaN   NaN   \n",
       "2010-01-04     NaN     NaN     NaN     NaN   NaN   NaN    NaN   NaN  0.01   \n",
       "2010-01-05    0.05    0.01   -0.01    0.79  3.06  0.30   3.53 -0.69 -0.00   \n",
       "2010-01-06   -0.49   -0.19   -0.07   -2.44 -1.88  0.08   0.62 -0.19  0.00   \n",
       "2010-01-07   -0.06   -0.32   -0.20   -2.25  3.41  0.48   4.55 -0.10 -0.01   \n",
       "...            ...     ...     ...     ...   ...   ...    ...   ...   ...   \n",
       "2018-06-25   -2.75   -2.02   -1.79  -52.52 -4.48 -3.74 -37.81  3.56  0.00   \n",
       "2018-06-26    2.26    0.69   -1.04   27.94  0.04  0.60   5.99 -1.41 -0.01   \n",
       "2018-06-27   -0.27   -1.54   -0.91  -30.58 -1.40 -2.25 -23.43  1.99 -0.01   \n",
       "2018-06-28    1.34    1.09    0.49   40.94  3.24  1.54  16.68 -1.06  0.00   \n",
       "2018-06-29   -0.39   -0.02    0.46   -1.65 -2.85  0.39   2.06 -0.76  0.01   \n",
       "\n",
       "             XAU=   GDX   GLD  \n",
       "Date                           \n",
       "2010-01-01    NaN   NaN   NaN  \n",
       "2010-01-04  23.65   NaN   NaN  \n",
       "2010-01-05  -1.35  0.46 -0.10  \n",
       "2010-01-06  19.85  1.17  1.81  \n",
       "2010-01-07  -6.60 -0.24 -0.69  \n",
       "...           ...   ...   ...  \n",
       "2018-06-25  -3.49 -0.17 -0.45  \n",
       "2018-06-26  -6.36 -0.06 -0.63  \n",
       "2018-06-27  -7.02 -0.14 -0.68  \n",
       "2018-06-28  -3.74  0.12 -0.36  \n",
       "2018-06-29   4.37  0.38  0.43  \n",
       "\n",
       "[2216 rows x 12 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diff(1).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.pct_change().mean().plot(kind='bar', figsize=(12, 6), color=['r','g','b','y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "      <th>GDX</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.006144</td>\n",
       "      <td>1.021572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>1.001729</td>\n",
       "      <td>1.000323</td>\n",
       "      <td>0.999521</td>\n",
       "      <td>1.005900</td>\n",
       "      <td>1.017680</td>\n",
       "      <td>1.002647</td>\n",
       "      <td>1.003116</td>\n",
       "      <td>0.965569</td>\n",
       "      <td>0.997016</td>\n",
       "      <td>0.998795</td>\n",
       "      <td>1.009642</td>\n",
       "      <td>0.999089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>0.984094</td>\n",
       "      <td>0.993863</td>\n",
       "      <td>0.996646</td>\n",
       "      <td>0.981884</td>\n",
       "      <td>0.989327</td>\n",
       "      <td>1.000704</td>\n",
       "      <td>1.000546</td>\n",
       "      <td>0.990181</td>\n",
       "      <td>1.003062</td>\n",
       "      <td>1.017745</td>\n",
       "      <td>1.024289</td>\n",
       "      <td>1.016500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>0.998151</td>\n",
       "      <td>0.989665</td>\n",
       "      <td>0.990385</td>\n",
       "      <td>0.982987</td>\n",
       "      <td>1.019568</td>\n",
       "      <td>1.004221</td>\n",
       "      <td>1.004001</td>\n",
       "      <td>0.994781</td>\n",
       "      <td>0.993478</td>\n",
       "      <td>0.994203</td>\n",
       "      <td>0.995136</td>\n",
       "      <td>0.993812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL.O    MSFT.O    INTC.O    AMZN.O      GS.N       SPY  \\\n",
       "Date                                                                     \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-04       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-05  1.001729  1.000323  0.999521  1.005900  1.017680  1.002647   \n",
       "2010-01-06  0.984094  0.993863  0.996646  0.981884  0.989327  1.000704   \n",
       "2010-01-07  0.998151  0.989665  0.990385  0.982987  1.019568  1.004221   \n",
       "\n",
       "                .SPX      .VIX      EUR=      XAU=       GDX       GLD  \n",
       "Date                                                                    \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2010-01-04       NaN       NaN  1.006144  1.021572       NaN       NaN  \n",
       "2010-01-05  1.003116  0.965569  0.997016  0.998795  1.009642  0.999089  \n",
       "2010-01-06  1.000546  0.990181  1.003062  1.017745  1.024289  1.016500  \n",
       "2010-01-07  1.004001  0.994781  0.993478  0.994203  0.995136  0.993812  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#变化率\n",
    "(data/data.shift(1)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL.O</th>\n",
       "      <th>MSFT.O</th>\n",
       "      <th>INTC.O</th>\n",
       "      <th>AMZN.O</th>\n",
       "      <th>GS.N</th>\n",
       "      <th>SPY</th>\n",
       "      <th>.SPX</th>\n",
       "      <th>.VIX</th>\n",
       "      <th>EUR=</th>\n",
       "      <th>XAU=</th>\n",
       "      <th>GDX</th>\n",
       "      <th>GLD</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006125</td>\n",
       "      <td>0.021342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>0.001727</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>-0.000479</td>\n",
       "      <td>0.005883</td>\n",
       "      <td>0.017525</td>\n",
       "      <td>0.002644</td>\n",
       "      <td>0.003111</td>\n",
       "      <td>-0.035038</td>\n",
       "      <td>-0.002988</td>\n",
       "      <td>-0.001206</td>\n",
       "      <td>0.009595</td>\n",
       "      <td>-0.000911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>-0.016034</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>-0.003360</td>\n",
       "      <td>-0.018282</td>\n",
       "      <td>-0.010731</td>\n",
       "      <td>0.000704</td>\n",
       "      <td>0.000545</td>\n",
       "      <td>-0.009868</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.017589</td>\n",
       "      <td>0.023999</td>\n",
       "      <td>0.016365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.001850</td>\n",
       "      <td>-0.010389</td>\n",
       "      <td>-0.009662</td>\n",
       "      <td>-0.017160</td>\n",
       "      <td>0.019379</td>\n",
       "      <td>0.004212</td>\n",
       "      <td>0.003993</td>\n",
       "      <td>-0.005233</td>\n",
       "      <td>-0.006544</td>\n",
       "      <td>-0.005814</td>\n",
       "      <td>-0.004876</td>\n",
       "      <td>-0.006207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL.O    MSFT.O    INTC.O    AMZN.O      GS.N       SPY  \\\n",
       "Date                                                                     \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-04       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-05  0.001727  0.000323 -0.000479  0.005883  0.017525  0.002644   \n",
       "2010-01-06 -0.016034 -0.006156 -0.003360 -0.018282 -0.010731  0.000704   \n",
       "2010-01-07 -0.001850 -0.010389 -0.009662 -0.017160  0.019379  0.004212   \n",
       "\n",
       "                .SPX      .VIX      EUR=      XAU=       GDX       GLD  \n",
       "Date                                                                    \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2010-01-04       NaN       NaN  0.006125  0.021342       NaN       NaN  \n",
       "2010-01-05  0.003111 -0.035038 -0.002988 -0.001206  0.009595 -0.000911  \n",
       "2010-01-06  0.000545 -0.009868  0.003058  0.017589  0.023999  0.016365  \n",
       "2010-01-07  0.003993 -0.005233 -0.006544 -0.005814 -0.004876 -0.006207  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = np.log(data/data.shift(1))\n",
    "rets.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AAPL.O</th>\n",
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       "      <th>INTC.O</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2010-01-05</th>\n",
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       "      <td>0.000323</td>\n",
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       "      <td>0.005883</td>\n",
       "      <td>0.017525</td>\n",
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       "      <th>2010-01-06</th>\n",
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       "      <td>-0.005833</td>\n",
       "      <td>-0.003839</td>\n",
       "      <td>-0.012399</td>\n",
       "      <td>0.006795</td>\n",
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       "      <td>0.003656</td>\n",
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       "      <td>0.006195</td>\n",
       "      <td>0.037725</td>\n",
       "      <td>0.033594</td>\n",
       "      <td>0.015454</td>\n",
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       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>-0.016157</td>\n",
       "      <td>-0.016221</td>\n",
       "      <td>-0.013501</td>\n",
       "      <td>-0.029559</td>\n",
       "      <td>0.026174</td>\n",
       "      <td>0.007560</td>\n",
       "      <td>0.007649</td>\n",
       "      <td>-0.050138</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>0.031911</td>\n",
       "      <td>0.028718</td>\n",
       "      <td>0.009247</td>\n",
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       "      <th>2018-06-25</th>\n",
       "      <td>1.466786</td>\n",
       "      <td>1.018777</td>\n",
       "      <td>0.810415</td>\n",
       "      <td>2.329927</td>\n",
       "      <td>0.237974</td>\n",
       "      <td>0.794630</td>\n",
       "      <td>0.809084</td>\n",
       "      <td>-2.326198</td>\n",
       "      <td>-0.202107</td>\n",
       "      <td>0.092620</td>\n",
       "      <td>-0.864185</td>\n",
       "      <td>-0.020548</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>1.479115</td>\n",
       "      <td>1.025766</td>\n",
       "      <td>0.789693</td>\n",
       "      <td>2.346587</td>\n",
       "      <td>0.238155</td>\n",
       "      <td>0.796842</td>\n",
       "      <td>0.811286</td>\n",
       "      <td>-2.411061</td>\n",
       "      <td>-0.206990</td>\n",
       "      <td>0.087580</td>\n",
       "      <td>-0.866914</td>\n",
       "      <td>-0.025816</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>1.477650</td>\n",
       "      <td>1.010101</td>\n",
       "      <td>0.771202</td>\n",
       "      <td>2.328338</td>\n",
       "      <td>0.231816</td>\n",
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       "      <td>0.802645</td>\n",
       "      <td>-2.293278</td>\n",
       "      <td>-0.215008</td>\n",
       "      <td>0.081987</td>\n",
       "      <td>-0.873313</td>\n",
       "      <td>-0.031534</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>1.484900</td>\n",
       "      <td>1.021214</td>\n",
       "      <td>0.781201</td>\n",
       "      <td>2.352695</td>\n",
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       "      <td>-0.213710</td>\n",
       "      <td>0.078994</td>\n",
       "      <td>-0.867826</td>\n",
       "      <td>-0.034575</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>1.482796</td>\n",
       "      <td>1.021011</td>\n",
       "      <td>0.790498</td>\n",
       "      <td>2.351724</td>\n",
       "      <td>0.233586</td>\n",
       "      <td>0.795663</td>\n",
       "      <td>0.809562</td>\n",
       "      <td>-2.400439</td>\n",
       "      <td>-0.203732</td>\n",
       "      <td>0.082490</td>\n",
       "      <td>-0.850646</td>\n",
       "      <td>-0.030944</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2216 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL.O    MSFT.O    INTC.O    AMZN.O      GS.N       SPY  \\\n",
       "Date                                                                     \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-04       NaN       NaN       NaN       NaN       NaN       NaN   \n",
       "2010-01-05  0.001727  0.000323 -0.000479  0.005883  0.017525  0.002644   \n",
       "2010-01-06 -0.014307 -0.005833 -0.003839 -0.012399  0.006795  0.003347   \n",
       "2010-01-07 -0.016157 -0.016221 -0.013501 -0.029559  0.026174  0.007560   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
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       "2018-06-29  1.482796  1.021011  0.790498  2.351724  0.233586  0.795663   \n",
       "\n",
       "                .SPX      .VIX      EUR=      XAU=       GDX       GLD  \n",
       "Date                                                                    \n",
       "2010-01-01       NaN       NaN       NaN       NaN       NaN       NaN  \n",
       "2010-01-04       NaN       NaN  0.006125  0.021342       NaN       NaN  \n",
       "2010-01-05  0.003111 -0.035038  0.003137  0.020136  0.009595 -0.000911  \n",
       "2010-01-06  0.003656 -0.044906  0.006195  0.037725  0.033594  0.015454  \n",
       "2010-01-07  0.007649 -0.050138 -0.000349  0.031911  0.028718  0.009247  \n",
       "...              ...       ...       ...       ...       ...       ...  \n",
       "2018-06-25  0.809084 -2.326198 -0.202107  0.092620 -0.864185 -0.020548  \n",
       "2018-06-26  0.811286 -2.411061 -0.206990  0.087580 -0.866914 -0.025816  \n",
       "2018-06-27  0.802645 -2.293278 -0.215008  0.081987 -0.873313 -0.031534  \n",
       "2018-06-28  0.808804 -2.354287 -0.213710  0.078994 -0.867826 -0.034575  \n",
       "2018-06-29  0.809562 -2.400439 -0.203732  0.082490 -0.850646 -0.030944  \n",
       "\n",
       "[2216 rows x 12 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2018-06-27</th>\n",
       "      <td>1.477650</td>\n",
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       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
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       "      <td>-0.867826</td>\n",
       "      <td>-0.034575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>1.482796</td>\n",
       "      <td>1.021011</td>\n",
       "      <td>0.790498</td>\n",
       "      <td>2.351724</td>\n",
       "      <td>0.233586</td>\n",
       "      <td>0.795663</td>\n",
       "      <td>0.809562</td>\n",
       "      <td>-2.400439</td>\n",
       "      <td>-0.203732</td>\n",
       "      <td>0.082490</td>\n",
       "      <td>-0.850646</td>\n",
       "      <td>-0.030944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AAPL.O    MSFT.O    INTC.O    AMZN.O      GS.N       SPY  \\\n",
       "Date                                                                     \n",
       "2018-06-25  1.466786  1.018777  0.810415  2.329927  0.237974  0.794630   \n",
       "2018-06-26  1.479115  1.025766  0.789693  2.346587  0.238155  0.796842   \n",
       "2018-06-27  1.477650  1.010101  0.771202  2.328338  0.231816  0.788523   \n",
       "2018-06-28  1.484900  1.021214  0.781201  2.352695  0.246424  0.794224   \n",
       "2018-06-29  1.482796  1.021011  0.790498  2.351724  0.233586  0.795663   \n",
       "\n",
       "                .SPX      .VIX      EUR=      XAU=       GDX       GLD  \n",
       "Date                                                                    \n",
       "2018-06-25  0.809084 -2.326198 -0.202107  0.092620 -0.864185 -0.020548  \n",
       "2018-06-26  0.811286 -2.411061 -0.206990  0.087580 -0.866914 -0.025816  \n",
       "2018-06-27  0.802645 -2.293278 -0.215008  0.081987 -0.873313 -0.031534  \n",
       "2018-06-28  0.808804 -2.354287 -0.213710  0.078994 -0.867826 -0.034575  \n",
       "2018-06-29  0.809562 -2.400439 -0.203732  0.082490 -0.850646 -0.030944  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(rets).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.cumsum(rets).apply(np.exp).plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-01-01           NaN\n",
       "2010-01-04     30.572827\n",
       "2010-01-05     30.625684\n",
       "2010-01-06     30.138541\n",
       "2010-01-07     30.082827\n",
       "                 ...    \n",
       "2018-06-25    182.170000\n",
       "2018-06-26    184.430000\n",
       "2018-06-27    184.160000\n",
       "2018-06-28    185.500000\n",
       "2018-06-29    185.110000\n",
       "Name: AAPL.O, Length: 2216, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['AAPL.O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data2 = data.rolling(window=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data2.dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "short = range(30, 51, 2)\n",
    "long = range(180, 281, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data[['AAPL.O']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['short'] = df['AAPL.O'].rolling(window=50).mean()\n",
    "df['long'] = df['AAPL.O'].rolling(window=250).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['position'] = np.where(df.short > df.long, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['short', 'long', 'position']].plot(figsize=(10,6), secondary_y='position')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['rets'] = np.log(df['AAPL.O']/df['AAPL.O'].shift(1))\n",
    "df['strategy'] = df['position'].shift(1) * df['rets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rets        3.981231\n",
       "strategy    4.412739\n",
       "dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(df[['rets', 'strategy']].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-12-29</th>\n",
       "      <td>44.967176</td>\n",
       "      <td>37.048433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-30</th>\n",
       "      <td>45.007662</td>\n",
       "      <td>37.111090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>45.042034</td>\n",
       "      <td>37.172907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-03</th>\n",
       "      <td>45.099319</td>\n",
       "      <td>37.240679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-04</th>\n",
       "      <td>45.167376</td>\n",
       "      <td>37.309656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-25</th>\n",
       "      <td>183.154200</td>\n",
       "      <td>168.453480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-26</th>\n",
       "      <td>183.326400</td>\n",
       "      <td>168.607880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-27</th>\n",
       "      <td>183.444800</td>\n",
       "      <td>168.769800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-28</th>\n",
       "      <td>183.598000</td>\n",
       "      <td>168.935720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-06-29</th>\n",
       "      <td>183.844200</td>\n",
       "      <td>169.102160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 short        long\n",
       "Date                              \n",
       "2010-12-29   44.967176   37.048433\n",
       "2010-12-30   45.007662   37.111090\n",
       "2010-12-31   45.042034   37.172907\n",
       "2011-01-03   45.099319   37.240679\n",
       "2011-01-04   45.167376   37.309656\n",
       "...                ...         ...\n",
       "2018-06-25  183.154200  168.453480\n",
       "2018-06-26  183.326400  168.607880\n",
       "2018-06-27  183.444800  168.769800\n",
       "2018-06-28  183.598000  168.935720\n",
       "2018-06-29  183.844200  169.102160\n",
       "\n",
       "[1889 rows x 2 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['short', 'long']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n",
      "C:\\Users\\Bnnev\\AppData\\Local\\Temp\\ipykernel_23600\\3151330980.py:18: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  rets = rets.append(pd.DataFrame(\n"
     ]
    }
   ],
   "source": [
    "short = range(30,51,2)\n",
    "long = range(180,281,10)\n",
    "rets = pd.DataFrame()\n",
    "for m,n in product(short,long):\n",
    "    apple = data[['AAPL.O']].copy()\n",
    "    apple.dropna(inplace=True)\n",
    "\n",
    "    apple['m'] = apple['AAPL.O'].rolling(m).mean()\n",
    "    apple['n'] = apple['AAPL.O'].rolling(n).mean()\n",
    "    apple.dropna(inplace=True)\n",
    "    apple['pos'] = np.where(apple['m'] > apple['n'], 1, -1) \n",
    "    apple['ret'] = np.log(apple['AAPL.O']/apple['AAPL.O'].shift(1))\n",
    "\n",
    "    apple['stra'] = apple['pos'].shift(1) * apple['ret']\n",
    "    apple.dropna(inplace=True)\n",
    "    \n",
    "    profit = np.exp(apple[['ret', 'stra']].sum())\n",
    "    rets = rets.append(pd.DataFrame(\n",
    "        {'m':m,\n",
    "         'n':n,\n",
    "         'rets': profit['ret'],\n",
    "         'stra': profit['stra'],\n",
    "         'gains': profit['stra'] - profit['ret']\n",
    "        },\n",
    "        index = [0]), ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>m</th>\n",
       "      <th>n</th>\n",
       "      <th>rets</th>\n",
       "      <th>stra</th>\n",
       "      <th>gains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>180</td>\n",
       "      <td>4.574979</td>\n",
       "      <td>4.880920</td>\n",
       "      <td>0.305941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>190</td>\n",
       "      <td>4.650342</td>\n",
       "      <td>5.769667</td>\n",
       "      <td>1.119325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>200</td>\n",
       "      <td>4.074753</td>\n",
       "      <td>6.125835</td>\n",
       "      <td>2.051083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30</td>\n",
       "      <td>210</td>\n",
       "      <td>4.259883</td>\n",
       "      <td>5.466799</td>\n",
       "      <td>1.206916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>220</td>\n",
       "      <td>4.220272</td>\n",
       "      <td>5.175521</td>\n",
       "      <td>0.955249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>50</td>\n",
       "      <td>240</td>\n",
       "      <td>4.045619</td>\n",
       "      <td>4.571074</td>\n",
       "      <td>0.525455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>50</td>\n",
       "      <td>250</td>\n",
       "      <td>3.983434</td>\n",
       "      <td>4.410298</td>\n",
       "      <td>0.426864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>50</td>\n",
       "      <td>260</td>\n",
       "      <td>3.762184</td>\n",
       "      <td>4.162091</td>\n",
       "      <td>0.399907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>50</td>\n",
       "      <td>270</td>\n",
       "      <td>3.775447</td>\n",
       "      <td>4.047360</td>\n",
       "      <td>0.271912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>50</td>\n",
       "      <td>280</td>\n",
       "      <td>3.654796</td>\n",
       "      <td>3.852668</td>\n",
       "      <td>0.197872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      m    n      rets      stra     gains\n",
       "0    30  180  4.574979  4.880920  0.305941\n",
       "1    30  190  4.650342  5.769667  1.119325\n",
       "2    30  200  4.074753  6.125835  2.051083\n",
       "3    30  210  4.259883  5.466799  1.206916\n",
       "4    30  220  4.220272  5.175521  0.955249\n",
       "..   ..  ...       ...       ...       ...\n",
       "116  50  240  4.045619  4.571074  0.525455\n",
       "117  50  250  3.983434  4.410298  0.426864\n",
       "118  50  260  3.762184  4.162091  0.399907\n",
       "119  50  270  3.775447  4.047360  0.271912\n",
       "120  50  280  3.654796  3.852668  0.197872\n",
       "\n",
       "[121 rows x 5 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets"
   ]
  }
 ],
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